PPC distributions

Description

Compare the empirical distribution of the data y to the distributions of simulated/replicated data yrep from the posterior predictive distribution. See the Plot Descriptions section, below, for details.

Usage

ppc_data(y, yrep, group = NULL)

ppc_hist(y, yrep, ..., binwidth = NULL, breaks = NULL, freq = TRUE)

ppc_boxplot(y, yrep, ..., notch = TRUE, size = 0.5, alpha = 1)

ppc_freqpoly(y, yrep, ..., binwidth = NULL, freq = TRUE, size = 0.25,
  alpha = 1)

ppc_freqpoly_grouped(y, yrep, group, ..., binwidth = NULL, freq = TRUE,
  size = 0.25, alpha = 1)

ppc_dens(y, yrep, ..., trim = FALSE, size = 0.5, alpha = 1)

ppc_dens_overlay(y, yrep, ..., size = 0.25, alpha = 0.7, trim = FALSE,
  bw = "nrd0", adjust = 1, kernel = "gaussian", n_dens = 1024)

ppc_ecdf_overlay(y, yrep, ..., discrete = FALSE, pad = TRUE, size = 0.25,
  alpha = 0.7)

ppc_violin_grouped(y, yrep, group, ..., probs = c(0.1, 0.5, 0.9), size = 1,
  alpha = 1, y_draw = c("violin", "points", "both"), y_size = 1,
  y_alpha = 1, y_jitter = 0.1)

Arguments

Details

For Binomial data, the plots will typically be most useful if y and yrep contain the "success" proportions (not discrete "success" or "failure" counts).

Value

A ggplot object that can be further customized using the ggplot2 package. The _data functions return the data that would have been drawn by the plotting function.

Plot Descriptions

ppc_hist, ppc_freqpoly, ppc_dens, ppc_boxplot

A separate histogram, shaded frequency polygon, smoothed kernel density estimate, or box and whiskers plot is displayed for y and each dataset (row) in yrep. For these plots yrep should therefore contain only a small number of rows. See the Examples section.

ppc_freqpoly_grouped

A separate frequency polygon is plotted for each level of a grouping variable for y and each dataset (row) in yrep. For this plot yrep should therefore contain only a small number of rows. See the Examples section.

ppc_dens_overlay, ppc_ecdf_overlay

Kernel density or empirical CDF estimates of each dataset (row) in yrep are overlaid, with the distribution of y itself on top (and in a darker shade). When using ppc_ecdf_overlay with discrete data set the discrete argument to TRUE for better results.

ppc_violin_grouped

The density estimate of yrep within each level of a grouping variable is plotted as a violin with horizontal lines at notable quantiles. y is overlaid on the plot either as a violin, points, or both, depending on the y_draw argument.

References

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). Bayesian Data Analysis. Chapman & Hall/CRC Press, London, third edition. (Ch. 6)

See Also

Other PPCs: PPC-discrete, PPC-errors, PPC-intervals, PPC-loo, PPC-overview, PPC-scatterplots, PPC-test-statistics

Examples

color_scheme_set("brightblue")
y <- example_y_data()
yrep <- example_yrep_draws()
dim(yrep)
ppc_dens_overlay(y, yrep[1:25, ])

ppc_ecdf_overlay(y, yrep[sample(nrow(yrep), 25), ])


# for ppc_hist,dens,freqpoly,boxplot definitely use a subset yrep rows so
# only a few (instead of nrow(yrep)) histograms are plotted
ppc_hist(y, yrep[1:8, ])


color_scheme_set("red")
ppc_boxplot(y, yrep[1:8, ])

# wizard hat plot
color_scheme_set("blue")
ppc_dens(y, yrep[200:202, ])


ppc_freqpoly(y, yrep[1:3,], alpha = 0.1, size = 1, binwidth = 5)

# if groups are different sizes then the 'freq' argument can be useful
group <- example_group_data()
ppc_freqpoly_grouped(y, yrep[1:3,], group) + yaxis_text()

ppc_freqpoly_grouped(y, yrep[1:3,], group, freq = FALSE) + yaxis_text()


# don't need to only use small number of rows for ppc_violin_grouped
# (as it pools yrep draws within groups)
color_scheme_set("gray")
ppc_violin_grouped(y, yrep, group, size = 1.5)

ppc_violin_grouped(y, yrep, group, alpha = 0)

# change how y is drawn
ppc_violin_grouped(y, yrep, group, alpha = 0, y_draw = "points", y_size = 1.5)
ppc_violin_grouped(y, yrep, group, alpha = 0, y_draw = "both",
                   y_size = 1.5, y_alpha = 0.5, y_jitter = 0.33)